Abstract and Applied Analysis

Regularized Least Square Regression with Unbounded and Dependent Sampling

Xiaorong Chu and Hongwei Sun

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Abstract

This paper mainly focuses on the least square regression problem for the α -mixing and ϕ -mixing processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from dependent sampling process with a more general output data condition. Capacity independent error bounds and learning rates are deduced by means of the integral operator technique.

Article information

Source
Abstr. Appl. Anal., Volume 2013 (2013), Article ID 139318, 7 pages.

Dates
First available in Project Euclid: 27 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393511839

Digital Object Identifier
doi:10.1155/2013/139318

Mathematical Reviews number (MathSciNet)
MR3044987

Zentralblatt MATH identifier
1273.62208

Citation

Chu, Xiaorong; Sun, Hongwei. Regularized Least Square Regression with Unbounded and Dependent Sampling. Abstr. Appl. Anal. 2013 (2013), Article ID 139318, 7 pages. doi:10.1155/2013/139318. https://projecteuclid.org/euclid.aaa/1393511839


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